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Tarka-AIR
/
Tarka-Embedding-30M-V1

Feature Extraction
sentence-transformers
Safetensors
Transformers
kron_qwen3
sentence-similarity
text-embeddings-inference
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Tarka-AIR/Tarka-Embedding-30M-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Tarka-AIR/Tarka-Embedding-30M-V1 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Tarka-AIR/Tarka-Embedding-30M-V1", trust_remote_code=True)
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Transformers

    How to use Tarka-AIR/Tarka-Embedding-30M-V1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Tarka-AIR/Tarka-Embedding-30M-V1", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Tarka-AIR/Tarka-Embedding-30M-V1", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Tarka-Embedding-30M-V1
72.2 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
Jaswanth-0821's picture
Jaswanth-0821
Update README.md
d4e21e9 verified 5 months ago
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  • README.md
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  • added_tokens.json
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  • config.json
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  • config_sentence_transformers.json
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  • kron_qwen3.py
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  • merges.txt
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  • model.safetensors
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  • modules.json
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  • sentence_bert_config.json
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • vocab.json
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